Don't use _tol in tests (#1158)

* Don't use _tol in tests

* don't deprecate g_tol yet

Author: pkofod

src/api.jl

@@ -157,6 +157,8 @@ f_relchange(r::MultivariateOptimizationResults) = r.f_relchange
 
 g_tol(r::OptimizationResults) = error("g_tol is not implemented for $(summary(r)).")
 g_tol(r::MultivariateOptimizationResults) = r.g_abstol
+g_abstol(r::OptimizationResults) = error("g_tol is not implemented for $(summary(r)).")
+g_abstol(r::MultivariateOptimizationResults) = r.g_abstol
 g_residual(r::MultivariateOptimizationResults) = r.g_residual
 
 

src/types.jl

@@ -113,9 +113,7 @@ function Options(;
         x_abstol = x_tol
     end
     if !(g_tol === nothing)
-        @warn(
-            lazy"g_tol is deprecated. Use g_abstol instead. The provided value ($(g_tol)) will be used as g_abstol.",
-        )
+        # lets deprecate this when reltol is introduced
         g_abstol = g_tol
     end
     if !(f_tol === nothing)

test/general/api.jl

@@ -82,21 +82,21 @@
     optimize(f, g!, h!, initial_x, SimulatedAnnealing())
 
     options = Optim.Options(
-        g_tol = 1e-12,
+        g_abstol = 1e-12,
         iterations = 10,
         store_trace = true,
         show_trace = false,
     )
     res = optimize(f, g!, h!, initial_x, BFGS(), options)
 
     options_g = Optim.Options(
-        g_tol = 1e-12,
+        g_abstol = 1e-12,
         iterations = 10,
         store_trace = true,
         show_trace = false,
     )
     options_f = Optim.Options(
-        g_tol = 1e-12,
+        g_abstol = 1e-12,
         iterations = 10,
         store_trace = true,
         show_trace = false,
@@ -114,7 +114,7 @@
 
     res = optimize(f, g!, h!, initial_x, BFGS(), options_g)
     options_ext = Optim.Options(
-        g_tol = 1e-12,
+        g_abstol = 1e-12,
         iterations = 10,
         store_trace = true,
         show_trace = false,
@@ -132,7 +132,7 @@
     @test Optim.x_converged(res) == false
     @test Optim.f_converged(res) == false
     @test Optim.g_converged(res) == false
-    @test Optim.g_tol(res) == 1e-12
+    @test Optim.g_abstol(res) == 1e-12
     @test Optim.iteration_limit_reached(res) == true
     @test Optim.initial_state(res) == [-1.2, 1.0]
     @test haskey(Optim.trace(res_ext)[1].metadata, "x")
@@ -160,7 +160,7 @@
 
 
 
-    resgterm = optimize(f, g!, initial_x, BFGS(), Optim.Options(g_tol = 1e12))
+    resgterm = optimize(f, g!, initial_x, BFGS(), Optim.Options(g_abstol = 1e12))
     Optim.termination_code(resgterm) == Optim.TerminationCode.GradientNorm
 
     resx0term = optimize(
@@ -170,7 +170,7 @@
         BFGS(),
         Optim.Options(
             x_reltol = -1,
-            g_tol = -1,
+            g_abstol = -1,
             f_abstol = -1,
             f_reltol = -1,
             iterations = 10^10,
@@ -187,7 +187,7 @@
         BFGS(),
         Optim.Options(
             x_abstol = -1,
-            g_tol = -1,
+            g_abstol = -1,
             f_abstol = -1,
             f_reltol = -1,
             iterations = 10^10,
@@ -205,7 +205,7 @@
         Optim.Options(
             x_abstol = 1e-3,
             x_reltol = -1,
-            g_tol = -1,
+            g_abstol = -1,
             f_abstol = -1,
             f_reltol = -1,
             iterations = 10^10,
@@ -221,7 +221,7 @@
         Optim.Options(
             x_abstol = -1,
             x_reltol = 1e-3,
-            g_tol = -1,
+            g_abstol = -1,
             f_abstol = -1,
             f_reltol = -1,
             iterations = 10^10,
@@ -258,7 +258,7 @@ end
     @test_throws ErrorException Optim.g_converged(res)
     @test_throws ErrorException Optim.x_tol(res)
     @test_throws ErrorException Optim.f_tol(res)
-    @test_throws ErrorException Optim.g_tol(res)
+    @test_throws ErrorException Optim.g_abstol(res)
     res = optimize(f, -2.0, 1.0, GoldenSection(), store_trace = true, extended_trace = true)
 
     # Right now, these just "test" if they run

test/general/optimize.jl

@@ -88,7 +88,7 @@ end
     end
 
     # To set tight tolerance on gradient g, need to disable any check on f
-    options = Optim.Options(g_tol = 1e-10, f_reltol = NaN, f_abstol = NaN)
+    options = Optim.Options(g_abstol = 1e-10, f_reltol = NaN, f_abstol = NaN)
     result = Optim.optimize(
         rosenbrock,
         g_rosenbrock!,

test/multivariate/manifolds.jl

@@ -35,7 +35,7 @@ using StableRNGs
                     gmanif!,
                     x0,
                     method(manifold = manif, linesearch = ls()),
-                    Optim.Options(allow_f_increases = true, g_tol = 1e-6),
+                    Optim.Options(allow_f_increases = true, g_abstol = 1e-6),
                 )
                 debug_printing && printstyled("Iter\tf-calls\tg-calls\n", color = :green)
                 debug_printing && printstyled(

test/multivariate/optimize/interface.jl

@@ -28,7 +28,7 @@
         fgh_res = []
         push!(fgh_res, optimize(tup..., problem.initial_x))
         for m in (NelderMead(), LBFGS(), Newton())
-            push!(fgh_res, optimize(tup..., problem.initial_x; f_tol = 1e-8))
+            push!(fgh_res, optimize(tup..., problem.initial_x; f_abstol = 1e-8))
             push!(fgh_res, optimize(tup..., problem.initial_x, m))
             push!(fgh_res, optimize(tup..., problem.initial_x, m, Optim.Options()))
         end

test/multivariate/successive_f_tol.jl

@@ -3,7 +3,7 @@
         alphaguess = LineSearches.InitialStatic(),
         linesearch = LineSearches.Static(),
     )
-    opt = Optim.Options(iterations = 10, successive_f_tol = 5, f_tol = 3, g_tol = -1)
+    opt = Optim.Options(iterations = 10, successive_f_tol = 5, f_abstol = 3, g_tol = -1)
     result = Optim.optimize(sum, (y, _) -> fill!(y, 1), [0.0, 0.0], alg, opt)
     @test result.iterations == opt.successive_f_tol + 1
 end